
#mendatory  pips to install
%pip install pydicom
%pip install opencv-python
%pip install scikit-image

Requirement already satisfied: pydicom in c:\anacondapython\envs\tensorflow\lib\site-packages (2.4.4)
Note: you may need to restart the kernel to use updated packages.
Requirement already satisfied: opencv-python in c:\anacondapython\envs\tensorflow\lib\site-packages (4.9.0.80)
Requirement already satisfied: numpy>=1.17.3 in c:\anacondapython\envs\tensorflow\lib\site-packages (from opencv-python) (1.22.3)
Note: you may need to restart the kernel to use updated packages.
Requirement already satisfied: scikit-image in c:\anacondapython\envs\tensorflow\lib\site-packages (0.22.0)
Requirement already satisfied: scipy>=1.8 in c:\anacondapython\envs\tensorflow\lib\site-packages (from scikit-image) (1.8.0)
Requirement already satisfied: tifffile>=2022.8.12 in c:\anacondapython\envs\tensorflow\lib\site-packages (from scikit-image) (2024.2.12)
Requirement already satisfied: pillow>=9.0.1 in c:\anacondapython\envs\tensorflow\lib\site-packages (from scikit-image) (9.0.1)
Requirement already satisfied: lazy_loader>=0.3 in c:\anacondapython\envs\tensorflow\lib\site-packages (from scikit-image) (0.3)
Requirement already satisfied: numpy>=1.22 in c:\anacondapython\envs\tensorflow\lib\site-packages (from scikit-image) (1.22.3)
Requirement already satisfied: networkx>=2.8 in c:\anacondapython\envs\tensorflow\lib\site-packages (from scikit-image) (3.2.1)
Requirement already satisfied: imageio>=2.27 in c:\anacondapython\envs\tensorflow\lib\site-packages (from scikit-image) (2.34.0)
Requirement already satisfied: packaging>=21 in c:\anacondapython\envs\tensorflow\lib\site-packages (from scikit-image) (21.3)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in c:\anacondapython\envs\tensorflow\lib\site-packages (from packaging>=21->scikit-image) (3.0.4)
Note: you may need to restart the kernel to use updated packages.


#import library core
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import os
import seaborn as sns
#read dcm's
import pydicom as pdcm

#import zip 
import zipfile as zp

#import cv2 open cv
import cv2

#import image checker
from PIL import Image
from skimage import measure, morphology
from skimage.feature import graycomatrix, graycoprops 

#import math
import math


#lets begin with train csv convert to dataframe
train_df = pd.read_csv('stage_2_train_labels.csv')
train_df


print(train_df.info())

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 30227 entries, 0 to 30226
Data columns (total 6 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   patientId  30227 non-null  object 
 1   x          9555 non-null   float64
 2   y          9555 non-null   float64
 3   width      9555 non-null   float64
 4   height     9555 non-null   float64
 5   Target     30227 non-null  int64  
dtypes: float64(4), int64(1), object(1)
memory usage: 1.4+ MB
None


def printwithinfoseparator(data,label):
    print(f'--------====== {label} =====-----------')
    print(data)
    print(f'---------===========-----------')


#to understand the information 


printwithinfoseparator(train_df['Target'].value_counts(),'Define by different target')
printwithinfoseparator(train_df[train_df['Target']== 0].isnull().sum(),'to check data with target 0 and not empty')
printwithinfoseparator(train_df[train_df['Target']== 1].isnull().sum(),'to check data with target 1 and not empty')

--------====== Define by different target =====-----------
0    20672
1     9555
Name: Target, dtype: int64
---------===========-----------
--------====== to check data with target 0 and not empty =====-----------
patientId        0
x            20672
y            20672
width        20672
height       20672
Target           0
dtype: int64
---------===========-----------
--------====== to check data with target 1 and not empty =====-----------
patientId    0
x            0
y            0
width        0
height       0
Target       0
dtype: int64
---------===========-----------


printwithinfoseparator(len(train_df[train_df['Target']== 1]['patientId'].unique()),'count unique with TARGET 1 ')
printwithinfoseparator(len(train_df[train_df['Target']== 0]['patientId'].unique()),'count unique with TARGET 0 ')

--------====== count unique with TARGET 1  =====-----------
6012
---------===========-----------
--------====== count unique with TARGET 0  =====-----------
20672
---------===========-----------


#if you have zip and not have unzipped then and then open it otherwise it will eat you 20 min.

## for train images zip
with zp.ZipFile("stage_2_train_images.zip","r") as zip_ref:
    zip_ref.extractall("train_images")

# for test images zip
with zp.ZipFile("stage_2_test_images.zip","r") as zip_ref:
    zip_ref.extractall("test_images")


#just extracted  traine images file and dumped to list
images_df = os.listdir('train_images\stage_2_train_images')
printwithinfoseparator(len(images_df),'Total trained images found from zip ')

--------====== Total trained images found from zip  =====-----------
26684
---------===========-----------


print('double check time --> total unique patientid and total images found both numbers are same :) :D ')

double check time --> total unique patientid and total images found both numbers are same :) :D


images_test_df = os.listdir('test_images\stage_2_test_images')
printwithinfoseparator(len(images_test_df),'Total test images found from zip ')

--------====== Total test images found from zip  =====-----------
3000
---------===========-----------


#now checking with second detail class csv and dump into dataframe
df_class = pd.read_csv('stage_2_detailed_class_info.csv')
df_class.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 30227 entries, 0 to 30226
Data columns (total 2 columns):
 #   Column     Non-Null Count  Dtype 
---  ------     --------------  ----- 
 0   patientId  30227 non-null  object
 1   class      30227 non-null  object
dtypes: object(2)
memory usage: 472.4+ KB


#need to merge both our dataframe based on patient id and on inner join 

mergedf=  pd.merge(train_df, df_class, on="patientId")
#remove the duplicated rows 
mergedf = mergedf.drop_duplicates()
mergedf


'''define a function to change the type from object / catagorical to int '''
def changeType(x):
   if x == 'No Lung Opacity / Not Normal':
       return 2
   elif x == 'Normal':
       return 0
   elif x == 'Lung Opacity':
       return 1


# apply the function to class col
mergedf['class']=mergedf['class'].apply(changeType)
mergedf


#find the value count for the df 
printwithinfoseparator(mergedf['class'].value_counts(),'merge dataframe with unique class and count')
# mergedf['class'].value_counts()

--------====== merge dataframe with unique class and count =====-----------
2    11821
1     9555
0     8851
Name: class, dtype: int64
---------===========-----------


#to verify the data merge has class 1 and target 1 both have other labeling box data available
data_check = mergedf[(mergedf['class']== 1 )&( mergedf['Target'] ==1)]
data_check


#by data check info we can check is there any missing data or now so we can eliminate or manipulate further
data_check.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 9555 entries, 4 to 37627
Data columns (total 7 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   patientId  9555 non-null   object 
 1   x          9555 non-null   float64
 2   y          9555 non-null   float64
 3   width      9555 non-null   float64
 4   height     9555 non-null   float64
 5   Target     9555 non-null   int64  
 6   class      9555 non-null   int64  
dtypes: float64(4), int64(2), object(1)
memory usage: 597.2+ KB


printwithinfoseparator(len(data_check['patientId'].unique()),'value with unique patient id in target')
print('which sows that some patientid have more then one bounding box present')

--------====== value with unique patient id in target =====-----------
6012
---------===========-----------
which sows that some patientid have more then one bounding box present


#plot the class wise classification 
plt.figure(figsize=(7,5),dpi=256)
fig,ax = plt.subplots(figsize=(7,5))
ax.bar(['No Lung Opacity/Not Normal','Lung Opacity','Normal'],mergedf['class'].value_counts().values,color=['orange','red','green'])
ax.tick_params(axis='x', rotation=15)
plt.xlabel('Class')
plt.ylabel('counts')
plt.title('Class wise data')
plt.show()

<Figure size 1792x1280 with 0 Axes>


#read images dicom and get the info 
first_dicom_file = images_df[0]
first_dicom_file_path = os.path.join('train_images\\stage_2_train_images\\', first_dicom_file)
dcm = pdcm.dcmread(first_dicom_file_path)
dcm.fix_meta_info

<bound method Dataset.fix_meta_info of Dataset.file_meta -------------------------------
(0002, 0000) File Meta Information Group Length  UL: 202
(0002, 0001) File Meta Information Version       OB: b'\x00\x01'
(0002, 0002) Media Storage SOP Class UID         UI: Secondary Capture Image Storage
(0002, 0003) Media Storage SOP Instance UID      UI: 1.2.276.0.7230010.3.1.4.8323329.28530.1517874485.775526
(0002, 0010) Transfer Syntax UID                 UI: JPEG Baseline (Process 1)
(0002, 0012) Implementation Class UID            UI: 1.2.276.0.7230010.3.0.3.6.0
(0002, 0013) Implementation Version Name         SH: 'OFFIS_DCMTK_360'
-------------------------------------------------
(0008, 0005) Specific Character Set              CS: 'ISO_IR 100'
(0008, 0016) SOP Class UID                       UI: Secondary Capture Image Storage
(0008, 0018) SOP Instance UID                    UI: 1.2.276.0.7230010.3.1.4.8323329.28530.1517874485.775526
(0008, 0020) Study Date                          DA: '19010101'
(0008, 0030) Study Time                          TM: '000000.00'
(0008, 0050) Accession Number                    SH: ''
(0008, 0060) Modality                            CS: 'CR'
(0008, 0064) Conversion Type                     CS: 'WSD'
(0008, 0090) Referring Physician's Name          PN: ''
(0008, 103e) Series Description                  LO: 'view: PA'
(0010, 0010) Patient's Name                      PN: '0004cfab-14fd-4e49-80ba-63a80b6bddd6'
(0010, 0020) Patient ID                          LO: '0004cfab-14fd-4e49-80ba-63a80b6bddd6'
(0010, 0030) Patient's Birth Date                DA: ''
(0010, 0040) Patient's Sex                       CS: 'F'
(0010, 1010) Patient's Age                       AS: '51'
(0018, 0015) Body Part Examined                  CS: 'CHEST'
(0018, 5101) View Position                       CS: 'PA'
(0020, 000d) Study Instance UID                  UI: 1.2.276.0.7230010.3.1.2.8323329.28530.1517874485.775525
(0020, 000e) Series Instance UID                 UI: 1.2.276.0.7230010.3.1.3.8323329.28530.1517874485.775524
(0020, 0010) Study ID                            SH: ''
(0020, 0011) Series Number                       IS: '1'
(0020, 0013) Instance Number                     IS: '1'
(0020, 0020) Patient Orientation                 CS: ''
(0028, 0002) Samples per Pixel                   US: 1
(0028, 0004) Photometric Interpretation          CS: 'MONOCHROME2'
(0028, 0010) Rows                                US: 1024
(0028, 0011) Columns                             US: 1024
(0028, 0030) Pixel Spacing                       DS: [0.14300000000000002, 0.14300000000000002]
(0028, 0100) Bits Allocated                      US: 8
(0028, 0101) Bits Stored                         US: 8
(0028, 0102) High Bit                            US: 7
(0028, 0103) Pixel Representation                US: 0
(0028, 2110) Lossy Image Compression             CS: '01'
(0028, 2114) Lossy Image Compression Method      CS: 'ISO_10918_1'
(7fe0, 0010) Pixel Data                          OB: Array of 142006 elements>


#just checking samples of train images 
pne_check = data_check['patientId'].sample(2)

for pni in pne_check:
    first_dicom_file_path = os.path.join('train_images\\stage_2_train_images\\', pni+'.dcm')
    dcm = pdcm.dcmread(first_dicom_file_path)
    plt.title(pni)
    plt.axis =0
    plt.imshow(dcm.pixel_array,cmap=plt.cm.binary) #used binary cmap
    plt.show()


#try to check for the sharpanning images with sharpen filter 

image_array = dcm.pixel_array

kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])

filtered_image =cv2.filter2D(image_array,-1,kernel)
plt.figure(figsize=(7,7),dpi=64)
plt.imshow((filtered_image),cmap=plt.cm.binary)
plt.show()
plt.figure(figsize=(7,7),dpi=64)
plt.imshow(dcm.pixel_array,cmap=plt.cm.binary)

<matplotlib.image.AxesImage at 0x1bb74963a30>


def createfolderifnot(path):
    if not os.path.exists(path):
        os.makedirs(path)
        print(f'path create {path}')


path_image_train = 'train_images\\images_jpg'
createfolderifnot(path_image_train)
path_image_train512 = 'train_images\\images_512_jpg'
createfolderifnot(path_image_train512)
path_image_train256 = 'train_images\\images_256_jpg'
createfolderifnot(path_image_train256)
path_image_train128 = 'train_images\\images_128_jpg'
createfolderifnot(path_image_train128)


info_df = mergedf.copy()
total_images = info_df['patientId'].unique()


info_df['AGE'] = 0
info_df['SEX'] = 0
info_df['ViewPosition']=''
info_df['BodyPart'] = ''
info_df['glcm_contrast'] = ''
info_df['glcm_homogeneity'] = ''
info_df['glcm_energy'] = ''
info_df['glcm_correlation'] = ''


## loop for the image to get meta data for age sex body part and view position
for i in total_images:
    path = f'train_images\\stage_2_train_images\\{i}.dcm'
    if os.path.isfile(path):
        first_dicom_file_path = os.path.join(path)
        dcm_read = pdcm.dcmread(first_dicom_file_path)
        img = Image.fromarray(dcm_read.pixel_array)
        img.save(f'{path_image_train}\\{i}.jpg')
        img2 = img.resize((512, 512))
        img2.save(f'{path_image_train512}\\{i}.jpg')
        img256 = img.resize((256, 256))
        img256.save(f'{path_image_train256}\\{i}.jpg')
        img128 = img.resize((128, 128))
        img128.save(f'{path_image_train128}\\{i}.jpg')
        image_array = np.array(img128)
    
        # Example: Thresholding to segment lung regions
        # threshold = -50  # Adjust threshold value based on DICOM pixel intensity range
        # lung_mask = image_array < threshold
        
        distances = [1, 2, 3]  # Adjust distances based on image resolution
        angles = [0, np.pi/4, np.pi/2, 3*np.pi/4]
        glcm = graycomatrix(image_array.astype(np.uint8), distances, angles, symmetric=True, normed=True)
        # print(glcm)
        for il in info_df[info_df['patientId']== i].index:
            print(f'image done {il}')
            info_df.loc[il,'AGE'] = dcm_read.PatientAge
            info_df.loc[il,'SEX'] = 0 if dcm_read.PatientSex == 'F' else 1
            info_df.loc[il,'BodyPart'] =dcm_read.BodyPartExamined
            info_df.loc[il,'ViewPosition'] =dcm_read.ViewPosition
            info_df.loc[il,'glcm_contrast'] =  graycoprops(glcm, 'contrast').mean()
            info_df.loc[il,'glcm_homogeneity'] = graycoprops(glcm, 'homogeneity').mean()
            info_df.loc[il,'glcm_energy'] = graycoprops(glcm, 'energy').mean()
            info_df.loc[il,'glcm_correlation'] = graycoprops(glcm, 'correlation').mean()

    else:
        print('nofile found ',i)

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info_df.to_csv('infodf.csv')


info_df = pd.read_csv('infodf.csv')
info_df = info_df[info_df.columns[1:]]
info_df


glcmcol = [ 'glcm_contrast', 'glcm_homogeneity','glcm_energy', 'glcm_correlation']
for i in glcmcol:
    info_df[i] = pd.to_numeric(info_df[i])

info_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 30227 entries, 0 to 30226
Data columns (total 15 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   patientId         30227 non-null  object 
 1   x                 9555 non-null   float64
 2   y                 9555 non-null   float64
 3   width             9555 non-null   float64
 4   height            9555 non-null   float64
 5   Target            30227 non-null  int64  
 6   class             30227 non-null  int64  
 7   AGE               30227 non-null  int64  
 8   SEX               30227 non-null  int64  
 9   ViewPosition      30227 non-null  object 
 10  BodyPart          30227 non-null  object 
 11  glcm_contrast     30227 non-null  float64
 12  glcm_homogeneity  30227 non-null  float64
 13  glcm_energy       30227 non-null  float64
 14  glcm_correlation  30227 non-null  float64
dtypes: float64(8), int64(4), object(3)
memory usage: 3.5+ MB


info_df.describe(include='all')


sns.pairplot(data=info_df)

<seaborn.axisgrid.PairGrid at 0x1bb0676b5e0>


for i in glcmcol:
    plt.title(f'box plot for {i}')
    sns.boxplot(data=info_df,x='Target',y=i)
    plt.show()


targetcols = ['Target','SEX','glcm_contrast', 'glcm_homogeneity','glcm_energy', 'glcm_correlation']
df_cor = info_df[targetcols]
sns.heatmap(data=df_cor.corr(),cmap=plt.cm.Blues,annot=True)

<AxesSubplot:>


printwithinfoseparator(info_df['BodyPart'].value_counts(),'body part value uniqueness check')
printwithinfoseparator(info_df['ViewPosition'].value_counts(),'view position value uniqueness check')
printwithinfoseparator(info_df['SEX'].value_counts(),'Sex value uniqueness check')

--------====== body part value uniqueness check =====-----------
CHEST    30227
Name: BodyPart, dtype: int64
---------===========-----------
--------====== view position value uniqueness check =====-----------
AP    15297
PA    14930
Name: ViewPosition, dtype: int64
---------===========-----------
--------====== Sex value uniqueness check =====-----------
1    17216
0    13011
Name: SEX, dtype: int64
---------===========-----------


info_df['AGE'].describe()

count    30227.000000
mean        46.797764
std         16.892940
min          1.000000
25%         34.000000
50%         49.000000
75%         59.000000
max        155.000000
Name: AGE, dtype: float64


info_df_512 = info_df.copy()


size = 512
def calculatetheimageboundings(x):
    mainsize = 1024
    # print(x)
    if x > 0:
        divider = mainsize//size
        # print(divider)
        _x  = x//divider
        # print(_x)
        return _x


size=512
info_df_512['x'] = info_df_512['x'].apply(calculatetheimageboundings)
info_df_512['y'] = info_df_512['y'].apply(calculatetheimageboundings)
info_df_512['width'] = info_df_512['width'].apply(calculatetheimageboundings)
info_df_512['height'] = info_df_512['height'].apply(calculatetheimageboundings)


info_df_512


# function to draw bounding Box on images
def get_bb(imag,data_lab,patid):
  
  for i in range(len(data_lab)):
    if data_lab.iloc[i]['patientId']==patid and data_lab.iloc[i]['Target']==1:
      x= int(data_lab.iloc[i]['x'])
      y= int(data_lab.iloc[i]['y'])                                                     
      w= int(data_lab.iloc[i]['width'])
      h= int(data_lab.iloc[i]['height'])
      bb=cv2.rectangle(imag,(x,y),(x+w,y+h),(255,0,0),3)
      plt.imshow(bb,cmap=plt.cm.binary)
      
      
    elif data_lab.iloc[i]['patientId']==patid and data_lab.iloc[i]['Target']==0:
      plt.imshow(imag,cmap=plt.cm.binary) 
      
  plt.show()


patid_ref = info_df[info_df['Target'] == 1]['patientId'].sample(5)
patid_ref

21226    c10053b8-7bc8-4d8f-99f0-92d86a97ee2e
19774    b6db3041-1554-43ef-baca-58733b6717d6
12546    7fa10f67-8c38-4027-b7f6-50887f85ad43
17457    a6c364d2-b2d6-4b0b-a439-40b992ff65c7
14685    90b2b94e-efa8-4ff7-98e1-86ea3e33b575
Name: patientId, dtype: object


for i in patid_ref: 
    img1= cv2.imread(f'train_images\images_jpg\{i}.jpg')
    get_bb(img1,info_df,i)


for i in patid_ref: 
    img1= cv2.imread(f'train_images\images_512_jpg\{i}.jpg')
    get_bb(img1,info_df_512,i)


sns.set(style="whitegrid")

# Create the countplot
plt.figure(figsize=(10, 6))
sns.countplot(x='Target', hue='class', data=info_df)
plt.title('Relationship Between Class and Target')
plt.xlabel('Target')
plt.ylabel('Count')
plt.legend(title='Class')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()


sns.set(style="whitegrid")

# Create the countplot
plt.figure(figsize=(10, 6))
sns.countplot(x='Target', hue='SEX', data=info_df)
plt.title('Relationship Between Sex and Target')
plt.xlabel('Target')
plt.ylabel('Count')
plt.legend(title='Class')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()


lung_opacity_data = info_df[info_df['Target'] == 1]

# Plotting
plt.figure(figsize=(12, 8))
plt.subplots_adjust(hspace=0.5)

plt.subplot(2, 2, 1)
sns.kdeplot(data=lung_opacity_data, x='x', shade=True, color='blue')
plt.title('Density of x')

plt.subplot(2, 2, 2)
sns.kdeplot(data=lung_opacity_data, x='y', shade=True, color='green')
plt.title('Density of y')

plt.subplot(2, 2, 3)
sns.kdeplot(data=lung_opacity_data, x='width', shade=True, color='orange')
plt.title('Density of width')

plt.subplot(2, 2, 4)
sns.kdeplot(data=lung_opacity_data, x='height', shade=True, color='red')
plt.title('Density of height')

plt.show()

C:\Users\JAYPAT~1\AppData\Local\Temp/ipykernel_1632/2878436323.py:8: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(data=lung_opacity_data, x='x', shade=True, color='blue')
C:\Users\JAYPAT~1\AppData\Local\Temp/ipykernel_1632/2878436323.py:12: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(data=lung_opacity_data, x='y', shade=True, color='green')
C:\Users\JAYPAT~1\AppData\Local\Temp/ipykernel_1632/2878436323.py:16: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(data=lung_opacity_data, x='width', shade=True, color='orange')
C:\Users\JAYPAT~1\AppData\Local\Temp/ipykernel_1632/2878436323.py:20: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(data=lung_opacity_data, x='height', shade=True, color='red')


ax= plt.figure(figsize=(10,7),dpi=64)
info_df.hist(ax=ax)

C:\Users\JAYPAT~1\AppData\Local\Temp/ipykernel_1632/2122389849.py:2: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared.
  info_df.hist(ax=ax)

array([[<AxesSubplot:title={'center':'x'}>,
        <AxesSubplot:title={'center':'y'}>,
        <AxesSubplot:title={'center':'width'}>],
       [<AxesSubplot:title={'center':'height'}>,
        <AxesSubplot:title={'center':'Target'}>,
        <AxesSubplot:title={'center':'class'}>],
       [<AxesSubplot:title={'center':'AGE'}>,
        <AxesSubplot:title={'center':'SEX'}>,
        <AxesSubplot:title={'center':'glcm_contrast'}>],
       [<AxesSubplot:title={'center':'glcm_homogeneity'}>,
        <AxesSubplot:title={'center':'glcm_energy'}>,
        <AxesSubplot:title={'center':'glcm_correlation'}>]], dtype=object)


# Assuming df is your DataFrame containing the 'Target' column
target_counts = info_df['class'].value_counts()

# Plotting the distribution as a pie chart
plt.figure(figsize=(8, 8))
plt.pie(target_counts, labels=target_counts.index, autopct='%1.1f%%', colors=['yellow', 'red','green'])
plt.title('Distribution of Targets')
plt.show()


pos_bbox = info_df.query('Target==1')
pos_bbox.plot.scatter(x='x', y='y')
pos_bbox.plot.scatter(x='x', y='height')
pos_bbox.plot.scatter(x='x', y='width')

<AxesSubplot:xlabel='x', ylabel='width'>


plt.figure(figsize=(2,2))
sns.pairplot(data=info_df)

<seaborn.axisgrid.PairGrid at 0x1bb14ffb2e0>

<Figure size 144x144 with 0 Axes>


def agetocatagory(age):
    age = int(age)
    if age >= 1 and age <= 13:
        return 'child'
    elif age >= 14 and age <=19:
        return 'teenage'
    elif age >=20 and age <=45:
        return 'Young'
    elif age >=46 and age <=60:
        return 'old'
    elif age >=61:
        return 'vold'


info_df['agecat'] =''

info_df['agecat'] = info_df['AGE'].apply(agetocatagory)


#for checkign based on target and sex wise age catagory
info_df.groupby(['Target','SEX'])['agecat'].value_counts()

Target  SEX  agecat 
0       0    Young      3410
             old        3240
             vold       1916
             teenage     252
             child       198
        1    old        4109
             Young      4009
             vold       2802
             teenage     451
             child       285
1       0    Young      1488
             old        1405
             vold        812
             child       147
             teenage     143
        1    Young      2246
             old        1735
             vold       1122
             teenage     248
             child       209
Name: agecat, dtype: int64


plt.figure(figsize=(10, 6))
sns.countplot(x='Target', hue='agecat', data=info_df)
plt.title('Relationship Between age catagory and Target')

Text(0.5, 1.0, 'Relationship Between age catagory and Target')


# Assuming df is your DataFrame containing the 'Target' column
target_counts = info_df.query('Target ==0')['agecat'].value_counts()

# Plotting the distribution as a pie chart
plt.figure(figsize=(8, 8))
plt.pie(target_counts, labels=target_counts.index, autopct='%1.1f%%')
plt.title('Distribution of Targets')
plt.show()


target_counts = info_df.query('Target ==1')['agecat'].value_counts()

# Plotting the distribution as a pie chart
plt.figure(figsize=(8, 8))
plt.pie(target_counts, labels=target_counts.index, autopct='%1.1f%%')
plt.title('Distribution of Targets')
plt.show()


size = 256
info_df_256 = info_df.copy()


info_df_256['x'] = info_df_256['x'].apply(calculatetheimageboundings)
info_df_256['y'] = info_df_256['y'].apply(calculatetheimageboundings)
info_df_256['width'] = info_df_256['width'].apply(calculatetheimageboundings)
info_df_256['height'] = info_df_256['height'].apply(calculatetheimageboundings)
info_df_256


#for getting just 1000 images from each classes
target_not_phe = info_df_256[info_df_256['class']==0].sample(1000)
target_not_phe_not_op =  info_df_256[info_df_256['class']==2].sample(1000)
target_phe =info_df_256[info_df_256['class']==1].sample(1000)


combine_model_df_1k = pd.concat([target_not_phe,target_phe,target_not_phe_not_op],axis=0)

combine_model_df_1k.reset_index(drop=True)
combine_model_df_1k


from sklearn.model_selection import train_test_split
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D,GlobalAveragePooling2D ,MaxPooling2D, Flatten, Dense, Dropout
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping

# Assuming combine_model_df_1k contains the dataset with patientId and corresponding labels

# Define image dimensions and batch size
img_width, img_height = 256, 256
batch_size = 32

# Define the directory where the images are stored
image_directory = 'train_images/images_256_jpg'

# Convert numerical class labels to string representations
combine_model_df_1k['class'] = combine_model_df_1k['class'].astype(str)
combine_model_df_1k['patientId'] = combine_model_df_1k['patientId'] + '.jpg'
# Split the dataset into training and validation sets
train_df, val_df = train_test_split(combine_model_df_1k, test_size=0.2, random_state=77)
epocs_count = 25

datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2)

train_generator = datagen.flow_from_dataframe(
    dataframe=train_df,
    directory=image_directory,
    x_col="patientId",  # Use the column name for file paths
    y_col="class",      # Use the column name for labels
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='categorical',
    subset='training'
)

validation_generator = datagen.flow_from_dataframe(
    dataframe=val_df,
    directory=image_directory,
    x_col="patientId",  # Use the column name for file paths
    y_col="class",      # Use the column name for labels
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='categorical',
    subset='validation'
)



model_cnn_1 = Sequential()
model_cnn_1.add(Conv2D(9,(3,3),activation='relu',input_shape = (img_width,img_height,1)))
model_cnn_1.add(MaxPooling2D((2,2)))

model_cnn_1.add(Conv2D(16,(3,3),activation='relu'))
model_cnn_1.add(MaxPooling2D((2,2)))

model_cnn_1.add(Conv2D(32,(3,3),activation='relu'))
model_cnn_1.add(MaxPooling2D((2,2)))

# model_cnn_1.add(GlobalAveragePooling2D())

model_cnn_1.add(Flatten())

model_cnn_1.add(Dense(128,activation='relu'))
model_cnn_1.add(Dropout(0.4))

model_cnn_1.add(Dense(64,activation='relu'))
model_cnn_1.add(Dropout(0.5))

model_cnn_1.add(Dense(3,activation='softmax'))

# Compile the model_cnn_1
model_cnn_1.compile(optimizer='adam',
              loss='categorical_crossentropy',  # Use categorical cross-entropy for multi-class classification
              metrics=['accuracy'],)

# Define callbacks
checkpoint = ModelCheckpoint("best_model.h5", monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')
early_stopping = EarlyStopping(monitor='val_accuracy', patience=5, verbose=1, restore_best_weights=True)

# Train the model
history = model_cnn_1.fit(
    train_generator,
    steps_per_epoch=train_generator.samples // batch_size,
    epochs=epocs_count,
    validation_data=validation_generator,
    validation_steps=validation_generator.samples // batch_size,
    callbacks=[checkpoint, early_stopping]
)

Found 1920 validated image filenames belonging to 3 classes.
Found 120 validated image filenames belonging to 3 classes.
Epoch 1/25
60/60 [==============================] - ETA: 0s - loss: 1.1483 - accuracy: 0.3568
Epoch 1: val_accuracy improved from -inf to 0.50000, saving model to best_model.h5
60/60 [==============================] - 12s 189ms/step - loss: 1.1483 - accuracy: 0.3568 - val_loss: 1.0650 - val_accuracy: 0.5000
Epoch 2/25
60/60 [==============================] - ETA: 0s - loss: 1.0080 - accuracy: 0.4760
Epoch 2: val_accuracy improved from 0.50000 to 0.54167, saving model to best_model.h5
60/60 [==============================] - 3s 53ms/step - loss: 1.0080 - accuracy: 0.4760 - val_loss: 0.9844 - val_accuracy: 0.5417
Epoch 3/25
60/60 [==============================] - ETA: 0s - loss: 0.9541 - accuracy: 0.5307
Epoch 3: val_accuracy improved from 0.54167 to 0.55208, saving model to best_model.h5
60/60 [==============================] - 3s 53ms/step - loss: 0.9541 - accuracy: 0.5307 - val_loss: 0.9456 - val_accuracy: 0.5521
Epoch 4/25
59/60 [============================>.] - ETA: 0s - loss: 0.9510 - accuracy: 0.5344
Epoch 4: val_accuracy improved from 0.55208 to 0.59375, saving model to best_model.h5
60/60 [==============================] - 3s 52ms/step - loss: 0.9525 - accuracy: 0.5328 - val_loss: 0.9307 - val_accuracy: 0.5938
Epoch 5/25
60/60 [==============================] - ETA: 0s - loss: 0.8979 - accuracy: 0.5599
Epoch 5: val_accuracy did not improve from 0.59375
60/60 [==============================] - 3s 48ms/step - loss: 0.8979 - accuracy: 0.5599 - val_loss: 0.9248 - val_accuracy: 0.5208
Epoch 6/25
59/60 [============================>.] - ETA: 0s - loss: 0.8624 - accuracy: 0.5890
Epoch 6: val_accuracy did not improve from 0.59375
60/60 [==============================] - 3s 48ms/step - loss: 0.8626 - accuracy: 0.5875 - val_loss: 0.9595 - val_accuracy: 0.5104
Epoch 7/25
60/60 [==============================] - ETA: 0s - loss: 0.8266 - accuracy: 0.6214
Epoch 7: val_accuracy did not improve from 0.59375
60/60 [==============================] - 3s 48ms/step - loss: 0.8266 - accuracy: 0.6214 - val_loss: 0.9639 - val_accuracy: 0.5625
Epoch 8/25
59/60 [============================>.] - ETA: 0s - loss: 0.7875 - accuracy: 0.6324
Epoch 8: val_accuracy did not improve from 0.59375
60/60 [==============================] - 3s 48ms/step - loss: 0.7868 - accuracy: 0.6328 - val_loss: 0.9801 - val_accuracy: 0.5312
Epoch 9/25
60/60 [==============================] - ETA: 0s - loss: 0.7498 - accuracy: 0.6599
Epoch 9: val_accuracy did not improve from 0.59375
Restoring model weights from the end of the best epoch: 4.
60/60 [==============================] - 3s 48ms/step - loss: 0.7498 - accuracy: 0.6599 - val_loss: 0.9989 - val_accuracy: 0.5729
Epoch 9: early stopping


train_acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
train_loss = history.history['loss']
val_loss = history.history['val_loss']

# Create separate plots for accuracy and loss
plt.figure(figsize=(10, 5))

# Accuracy plot
plt.subplot(121)
plt.plot(train_acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.title('Model Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()

# Loss plot
plt.subplot(122)
plt.plot(train_loss, label='Training Loss')
plt.plot(val_loss, label='Validation Loss')
plt.title('Model Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()

plt.tight_layout()
plt.show()


#for getting just 2000 images from each classes
target_not_phe = info_df_256[info_df_256['class']==0].sample(2000)
target_not_phe_not_op =  info_df_256[info_df_256['class']==2].sample(2000)
target_phe =info_df_256[info_df_256['class']==1].sample(2000)


combine_model_df_2k = pd.concat([target_not_phe,target_phe,target_not_phe_not_op],axis=0)
combine_model_df_2k.reset_index(drop=True)
combine_model_df_2k


combine_model_df_2k['class'] = combine_model_df_2k['class'].astype(str)
combine_model_df_2k['patientId'] = combine_model_df_2k['patientId'] + '.jpg'
# Split the dataset into training and validation sets
train_df2, val_df2 = train_test_split(combine_model_df_2k, test_size=0.2, random_state=77)
epocs_count = 30

datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2)

train_generator = datagen.flow_from_dataframe(
    dataframe=train_df2,
    directory=image_directory,
    x_col="patientId",  # Use the column name for file paths
    y_col="class",      # Use the column name for labels
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='categorical',
    subset='training'
)

validation_generator = datagen.flow_from_dataframe(
    dataframe=val_df2,
    directory=image_directory,
    x_col="patientId",  # Use the column name for file paths
    y_col="class",      # Use the column name for labels
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='categorical',
    subset='validation'
)



model_cnn_2_k256 = Sequential()
model_cnn_2_k256.add(Conv2D(9,(3,3),activation='relu',input_shape = (img_width,img_height,1)))
model_cnn_2_k256.add(MaxPooling2D((2,2)))

model_cnn_2_k256.add(Conv2D(16,(3,3),activation='relu'))
model_cnn_2_k256.add(MaxPooling2D((2,2)))

model_cnn_2_k256.add(Conv2D(8,(3,3),activation='relu'))
model_cnn_2_k256.add(MaxPooling2D((2,2)))


model_cnn_2_k256.add(Flatten())

model_cnn_2_k256.add(Dense(64,activation='relu'))
model_cnn_2_k256.add(Dropout(0.5))

model_cnn_2_k256.add(Dense(16,activation='relu'))

model_cnn_2_k256.add(Dense(3,activation='softmax'))

# Compile the model_cnn_2_k256
model_cnn_2_k256.compile(optimizer='adam',
              loss='categorical_crossentropy',  # Use categorical cross-entropy for multi-class classification
              metrics=['accuracy'],)

# Define callbacks
checkpoint = ModelCheckpoint("best_model.h5", monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')
early_stopping = EarlyStopping(monitor='val_accuracy', patience=15, verbose=1, restore_best_weights=True)

# Train the model
history = model_cnn_2_k256.fit(
    train_generator,
    steps_per_epoch=train_generator.samples // batch_size,
    epochs=epocs_count,
    validation_data=validation_generator,
    validation_steps=validation_generator.samples // batch_size,
    callbacks=[checkpoint, early_stopping]
)

Found 3840 validated image filenames belonging to 3 classes.
Found 240 validated image filenames belonging to 3 classes.
Epoch 1/30
120/120 [==============================] - ETA: 0s - loss: 1.0063 - accuracy: 0.4891
Epoch 1: val_accuracy improved from -inf to 0.54911, saving model to best_model.h5
120/120 [==============================] - 21s 173ms/step - loss: 1.0063 - accuracy: 0.4891 - val_loss: 0.8915 - val_accuracy: 0.5491
Epoch 2/30
120/120 [==============================] - ETA: 0s - loss: 0.9204 - accuracy: 0.5549
Epoch 2: val_accuracy improved from 0.54911 to 0.63839, saving model to best_model.h5
120/120 [==============================] - 6s 47ms/step - loss: 0.9204 - accuracy: 0.5549 - val_loss: 0.7900 - val_accuracy: 0.6384
Epoch 3/30
120/120 [==============================] - ETA: 0s - loss: 0.8894 - accuracy: 0.5701
Epoch 3: val_accuracy did not improve from 0.63839
120/120 [==============================] - 6s 47ms/step - loss: 0.8894 - accuracy: 0.5701 - val_loss: 0.8262 - val_accuracy: 0.6339
Epoch 4/30
119/120 [============================>.] - ETA: 0s - loss: 0.8631 - accuracy: 0.5869
Epoch 4: val_accuracy did not improve from 0.63839
120/120 [==============================] - 6s 47ms/step - loss: 0.8640 - accuracy: 0.5867 - val_loss: 0.8181 - val_accuracy: 0.6027
Epoch 5/30
120/120 [==============================] - ETA: 0s - loss: 0.8259 - accuracy: 0.6154
Epoch 5: val_accuracy improved from 0.63839 to 0.66518, saving model to best_model.h5
120/120 [==============================] - 6s 47ms/step - loss: 0.8259 - accuracy: 0.6154 - val_loss: 0.7894 - val_accuracy: 0.6652
Epoch 6/30
120/120 [==============================] - ETA: 0s - loss: 0.8011 - accuracy: 0.6266
Epoch 6: val_accuracy did not improve from 0.66518
120/120 [==============================] - 5s 45ms/step - loss: 0.8011 - accuracy: 0.6266 - val_loss: 0.8105 - val_accuracy: 0.6205
Epoch 7/30
119/120 [============================>.] - ETA: 0s - loss: 0.7612 - accuracy: 0.6373
Epoch 7: val_accuracy did not improve from 0.66518
120/120 [==============================] - 5s 46ms/step - loss: 0.7620 - accuracy: 0.6375 - val_loss: 0.8308 - val_accuracy: 0.6295
Epoch 8/30
120/120 [==============================] - ETA: 0s - loss: 0.7377 - accuracy: 0.6589
Epoch 8: val_accuracy improved from 0.66518 to 0.67411, saving model to best_model.h5
120/120 [==============================] - 6s 47ms/step - loss: 0.7377 - accuracy: 0.6589 - val_loss: 0.7854 - val_accuracy: 0.6741
Epoch 9/30
119/120 [============================>.] - ETA: 0s - loss: 0.7022 - accuracy: 0.6757
Epoch 9: val_accuracy did not improve from 0.67411
120/120 [==============================] - 6s 46ms/step - loss: 0.7020 - accuracy: 0.6760 - val_loss: 0.8313 - val_accuracy: 0.6607
Epoch 10/30
120/120 [==============================] - ETA: 0s - loss: 0.6611 - accuracy: 0.7016
Epoch 10: val_accuracy did not improve from 0.67411
120/120 [==============================] - 6s 46ms/step - loss: 0.6611 - accuracy: 0.7016 - val_loss: 0.8341 - val_accuracy: 0.6429
Epoch 11/30
120/120 [==============================] - ETA: 0s - loss: 0.6268 - accuracy: 0.7232
Epoch 11: val_accuracy did not improve from 0.67411
120/120 [==============================] - 6s 46ms/step - loss: 0.6268 - accuracy: 0.7232 - val_loss: 0.9333 - val_accuracy: 0.6429
Epoch 12/30
120/120 [==============================] - ETA: 0s - loss: 0.5794 - accuracy: 0.7326
Epoch 12: val_accuracy did not improve from 0.67411
120/120 [==============================] - 6s 46ms/step - loss: 0.5794 - accuracy: 0.7326 - val_loss: 0.9030 - val_accuracy: 0.6562
Epoch 13/30
120/120 [==============================] - ETA: 0s - loss: 0.5253 - accuracy: 0.7693
Epoch 13: val_accuracy did not improve from 0.67411
120/120 [==============================] - 6s 46ms/step - loss: 0.5253 - accuracy: 0.7693 - val_loss: 0.8976 - val_accuracy: 0.6741
Epoch 14/30
120/120 [==============================] - ETA: 0s - loss: 0.5027 - accuracy: 0.7755
Epoch 14: val_accuracy improved from 0.67411 to 0.68750, saving model to best_model.h5
120/120 [==============================] - 6s 51ms/step - loss: 0.5027 - accuracy: 0.7755 - val_loss: 0.8921 - val_accuracy: 0.6875
Epoch 15/30
120/120 [==============================] - ETA: 0s - loss: 0.4552 - accuracy: 0.7961
Epoch 15: val_accuracy did not improve from 0.68750
120/120 [==============================] - 6s 47ms/step - loss: 0.4552 - accuracy: 0.7961 - val_loss: 1.0401 - val_accuracy: 0.6741
Epoch 16/30
120/120 [==============================] - ETA: 0s - loss: 0.4218 - accuracy: 0.8167
Epoch 16: val_accuracy did not improve from 0.68750
120/120 [==============================] - 6s 46ms/step - loss: 0.4218 - accuracy: 0.8167 - val_loss: 0.9782 - val_accuracy: 0.6696
Epoch 17/30
120/120 [==============================] - ETA: 0s - loss: 0.3861 - accuracy: 0.8266
Epoch 17: val_accuracy did not improve from 0.68750
120/120 [==============================] - 6s 47ms/step - loss: 0.3861 - accuracy: 0.8266 - val_loss: 1.2267 - val_accuracy: 0.6384
Epoch 18/30
120/120 [==============================] - ETA: 0s - loss: 0.3565 - accuracy: 0.8440
Epoch 18: val_accuracy did not improve from 0.68750
120/120 [==============================] - 6s 47ms/step - loss: 0.3565 - accuracy: 0.8440 - val_loss: 1.0868 - val_accuracy: 0.6473
Epoch 19/30
120/120 [==============================] - ETA: 0s - loss: 0.3209 - accuracy: 0.8562
Epoch 19: val_accuracy did not improve from 0.68750
120/120 [==============================] - 6s 46ms/step - loss: 0.3209 - accuracy: 0.8562 - val_loss: 1.4054 - val_accuracy: 0.6205
Epoch 20/30
120/120 [==============================] - ETA: 0s - loss: 0.2985 - accuracy: 0.8695
Epoch 20: val_accuracy did not improve from 0.68750
120/120 [==============================] - 6s 46ms/step - loss: 0.2985 - accuracy: 0.8695 - val_loss: 1.3019 - val_accuracy: 0.6473
Epoch 21/30
119/120 [============================>.] - ETA: 0s - loss: 0.2857 - accuracy: 0.8713
Epoch 21: val_accuracy did not improve from 0.68750
120/120 [==============================] - 6s 46ms/step - loss: 0.2858 - accuracy: 0.8716 - val_loss: 1.4751 - val_accuracy: 0.6295
Epoch 22/30
120/120 [==============================] - ETA: 0s - loss: 0.2604 - accuracy: 0.8867
Epoch 22: val_accuracy did not improve from 0.68750
120/120 [==============================] - 6s 46ms/step - loss: 0.2604 - accuracy: 0.8867 - val_loss: 1.7375 - val_accuracy: 0.6027
Epoch 23/30
120/120 [==============================] - ETA: 0s - loss: 0.2586 - accuracy: 0.8935
Epoch 23: val_accuracy did not improve from 0.68750
120/120 [==============================] - 6s 46ms/step - loss: 0.2586 - accuracy: 0.8935 - val_loss: 1.3859 - val_accuracy: 0.6607
Epoch 24/30
120/120 [==============================] - ETA: 0s - loss: 0.2387 - accuracy: 0.8987
Epoch 24: val_accuracy did not improve from 0.68750
120/120 [==============================] - 6s 46ms/step - loss: 0.2387 - accuracy: 0.8987 - val_loss: 1.7400 - val_accuracy: 0.6562
Epoch 25/30
120/120 [==============================] - ETA: 0s - loss: 0.2301 - accuracy: 0.9021
Epoch 25: val_accuracy did not improve from 0.68750
120/120 [==============================] - 6s 46ms/step - loss: 0.2301 - accuracy: 0.9021 - val_loss: 1.5514 - val_accuracy: 0.6384
Epoch 26/30
119/120 [============================>.] - ETA: 0s - loss: 0.2079 - accuracy: 0.9086
Epoch 26: val_accuracy did not improve from 0.68750
120/120 [==============================] - 6s 46ms/step - loss: 0.2086 - accuracy: 0.9081 - val_loss: 1.7267 - val_accuracy: 0.6339
Epoch 27/30
120/120 [==============================] - ETA: 0s - loss: 0.2013 - accuracy: 0.9125
Epoch 27: val_accuracy did not improve from 0.68750
120/120 [==============================] - 6s 47ms/step - loss: 0.2013 - accuracy: 0.9125 - val_loss: 1.8162 - val_accuracy: 0.6339
Epoch 28/30
120/120 [==============================] - ETA: 0s - loss: 0.2143 - accuracy: 0.9047
Epoch 28: val_accuracy did not improve from 0.68750
120/120 [==============================] - 6s 53ms/step - loss: 0.2143 - accuracy: 0.9047 - val_loss: 1.9452 - val_accuracy: 0.5982
Epoch 29/30
120/120 [==============================] - ETA: 0s - loss: 0.1891 - accuracy: 0.9133
Epoch 29: val_accuracy did not improve from 0.68750
Restoring model weights from the end of the best epoch: 14.
120/120 [==============================] - 6s 47ms/step - loss: 0.1891 - accuracy: 0.9133 - val_loss: 1.6952 - val_accuracy: 0.6518
Epoch 29: early stopping


train_acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
train_loss = history.history['loss']
val_loss = history.history['val_loss']

# Create separate plots for accuracy and loss
plt.figure(figsize=(10, 5))

# Accuracy plot
plt.subplot(121)
plt.plot(train_acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.title('Model Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()

# Loss plot
plt.subplot(122)
plt.plot(train_loss, label='Training Loss')
plt.plot(val_loss, label='Validation Loss')
plt.title('Model Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()

plt.tight_layout()
plt.show()


#for getting just 3000 images from each classes
target_not_phe = info_df_256[info_df_256['class']==0].sample(3000)
target_not_phe_not_op =  info_df_256[info_df_256['class']==2].sample(3000)
target_phe =info_df_256[info_df_256['class']==1].sample(3000)



combine_model_df_3k = pd.concat([target_not_phe,target_phe,target_not_phe_not_op],axis=0)
combine_model_df_3k.reset_index(drop=True)
combine_model_df_3k


combine_model_df_3k['class'] = combine_model_df_3k['class'].astype(str)
combine_model_df_3k['patientId'] = combine_model_df_3k['patientId'] + '.jpg'
# Split the dataset into training and validation sets
train_df3, val_df3 = train_test_split(combine_model_df_3k, test_size=0.2, random_state=77)
epocs_count = 30

datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2)

train_generator = datagen.flow_from_dataframe(
    dataframe=train_df3,
    directory=image_directory,
    x_col="patientId",  # Use the column name for file paths
    y_col="class",      # Use the column name for labels
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='categorical',
    subset='training'
)

validation_generator = datagen.flow_from_dataframe(
    dataframe=val_df3,
    directory=image_directory,
    x_col="patientId",  # Use the column name for file paths
    y_col="class",      # Use the column name for labels
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='categorical',
    subset='validation'
)



model_cnn256_3k = Sequential()
model_cnn256_3k.add(Conv2D(9,(3,3),activation='relu',input_shape = (img_width,img_height,1)))
model_cnn256_3k.add(MaxPooling2D((2,2)))

model_cnn256_3k.add(Conv2D(16,(3,3),activation='relu'))
model_cnn256_3k.add(MaxPooling2D((2,2)))

model_cnn256_3k.add(Conv2D(32,(3,3),activation='relu'))
model_cnn256_3k.add(MaxPooling2D((2,2)))


model_cnn256_3k.add(Flatten())

model_cnn256_3k.add(Dense(64,activation='relu'))
model_cnn256_3k.add(Dropout(0.5))

model_cnn256_3k.add(Dense(32,activation='relu'))

model_cnn256_3k.add(Dense(3,activation='softmax'))

# Compile the model_cnn256_3k
model_cnn256_3k.compile(optimizer='adam',
              loss='categorical_crossentropy',  # Use categorical cross-entropy for multi-class classification
              metrics=['accuracy'],)

# Define callbacks
checkpoint = ModelCheckpoint("best_model.h5", monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')
early_stopping = EarlyStopping(monitor='val_accuracy', patience=15, verbose=1, restore_best_weights=True)

# Train the model
history = model_cnn256_3k.fit(
    train_generator,
    steps_per_epoch=train_generator.samples // batch_size,
    epochs=epocs_count,
    validation_data=validation_generator,
    validation_steps=validation_generator.samples // batch_size,
    callbacks=[checkpoint, early_stopping]
)

Found 5760 validated image filenames belonging to 3 classes.
Found 360 validated image filenames belonging to 3 classes.
Epoch 1/30
180/180 [==============================] - ETA: 0s - loss: 1.0452 - accuracy: 0.4443
Epoch 1: val_accuracy improved from -inf to 0.50568, saving model to best_model.h5
180/180 [==============================] - 28s 153ms/step - loss: 1.0452 - accuracy: 0.4443 - val_loss: 0.9424 - val_accuracy: 0.5057
Epoch 2/30
179/180 [============================>.] - ETA: 0s - loss: 0.9302 - accuracy: 0.5386
Epoch 2: val_accuracy improved from 0.50568 to 0.55966, saving model to best_model.h5
180/180 [==============================] - 9s 48ms/step - loss: 0.9309 - accuracy: 0.5382 - val_loss: 0.8922 - val_accuracy: 0.5597
Epoch 3/30
179/180 [============================>.] - ETA: 0s - loss: 0.9038 - accuracy: 0.5567
Epoch 3: val_accuracy improved from 0.55966 to 0.58523, saving model to best_model.h5
180/180 [==============================] - 9s 48ms/step - loss: 0.9030 - accuracy: 0.5569 - val_loss: 0.8429 - val_accuracy: 0.5852
Epoch 4/30
180/180 [==============================] - ETA: 0s - loss: 0.8783 - accuracy: 0.5681
Epoch 4: val_accuracy improved from 0.58523 to 0.59943, saving model to best_model.h5
180/180 [==============================] - 9s 51ms/step - loss: 0.8783 - accuracy: 0.5681 - val_loss: 0.8425 - val_accuracy: 0.5994
Epoch 5/30
179/180 [============================>.] - ETA: 0s - loss: 0.8425 - accuracy: 0.5938
Epoch 5: val_accuracy improved from 0.59943 to 0.61932, saving model to best_model.h5
180/180 [==============================] - 9s 50ms/step - loss: 0.8425 - accuracy: 0.5943 - val_loss: 0.8347 - val_accuracy: 0.6193
Epoch 6/30
180/180 [==============================] - ETA: 0s - loss: 0.8151 - accuracy: 0.6118
Epoch 6: val_accuracy did not improve from 0.61932
180/180 [==============================] - 9s 50ms/step - loss: 0.8151 - accuracy: 0.6118 - val_loss: 0.8385 - val_accuracy: 0.5994
Epoch 7/30
180/180 [==============================] - ETA: 0s - loss: 0.7842 - accuracy: 0.6280
Epoch 7: val_accuracy did not improve from 0.61932
180/180 [==============================] - 9s 48ms/step - loss: 0.7842 - accuracy: 0.6280 - val_loss: 0.8487 - val_accuracy: 0.6136
Epoch 8/30
179/180 [============================>.] - ETA: 0s - loss: 0.7569 - accuracy: 0.6369
Epoch 8: val_accuracy improved from 0.61932 to 0.63352, saving model to best_model.h5
180/180 [==============================] - 9s 49ms/step - loss: 0.7566 - accuracy: 0.6370 - val_loss: 0.8358 - val_accuracy: 0.6335
Epoch 9/30
179/180 [============================>.] - ETA: 0s - loss: 0.7156 - accuracy: 0.6625
Epoch 9: val_accuracy did not improve from 0.63352
180/180 [==============================] - 9s 48ms/step - loss: 0.7157 - accuracy: 0.6620 - val_loss: 0.8658 - val_accuracy: 0.6051
Epoch 10/30
180/180 [==============================] - ETA: 0s - loss: 0.6698 - accuracy: 0.6906
Epoch 10: val_accuracy did not improve from 0.63352
180/180 [==============================] - 9s 49ms/step - loss: 0.6698 - accuracy: 0.6906 - val_loss: 0.8274 - val_accuracy: 0.6307
Epoch 11/30
180/180 [==============================] - ETA: 0s - loss: 0.6334 - accuracy: 0.7023
Epoch 11: val_accuracy did not improve from 0.63352
180/180 [==============================] - 9s 48ms/step - loss: 0.6334 - accuracy: 0.7023 - val_loss: 0.8643 - val_accuracy: 0.6335
Epoch 12/30
180/180 [==============================] - ETA: 0s - loss: 0.5949 - accuracy: 0.7238
Epoch 12: val_accuracy did not improve from 0.63352
180/180 [==============================] - 9s 49ms/step - loss: 0.5949 - accuracy: 0.7238 - val_loss: 0.8698 - val_accuracy: 0.6307
Epoch 13/30
180/180 [==============================] - ETA: 0s - loss: 0.5585 - accuracy: 0.7347
Epoch 13: val_accuracy did not improve from 0.63352
180/180 [==============================] - 9s 48ms/step - loss: 0.5585 - accuracy: 0.7347 - val_loss: 0.9679 - val_accuracy: 0.6307
Epoch 14/30
179/180 [============================>.] - ETA: 0s - loss: 0.5137 - accuracy: 0.7622
Epoch 14: val_accuracy did not improve from 0.63352
180/180 [==============================] - 9s 48ms/step - loss: 0.5133 - accuracy: 0.7622 - val_loss: 0.9687 - val_accuracy: 0.6165
Epoch 15/30
180/180 [==============================] - ETA: 0s - loss: 0.4780 - accuracy: 0.7712
Epoch 15: val_accuracy improved from 0.63352 to 0.64773, saving model to best_model.h5
180/180 [==============================] - 11s 59ms/step - loss: 0.4780 - accuracy: 0.7712 - val_loss: 1.0108 - val_accuracy: 0.6477
Epoch 16/30
179/180 [============================>.] - ETA: 0s - loss: 0.4462 - accuracy: 0.7849
Epoch 16: val_accuracy did not improve from 0.64773
180/180 [==============================] - 9s 48ms/step - loss: 0.4461 - accuracy: 0.7847 - val_loss: 1.1305 - val_accuracy: 0.5966
Epoch 17/30
179/180 [============================>.] - ETA: 0s - loss: 0.4175 - accuracy: 0.7938
Epoch 17: val_accuracy did not improve from 0.64773
180/180 [==============================] - 9s 48ms/step - loss: 0.4175 - accuracy: 0.7937 - val_loss: 1.0877 - val_accuracy: 0.6222
Epoch 18/30
180/180 [==============================] - ETA: 0s - loss: 0.3996 - accuracy: 0.8068
Epoch 18: val_accuracy did not improve from 0.64773
180/180 [==============================] - 9s 48ms/step - loss: 0.3996 - accuracy: 0.8068 - val_loss: 1.1819 - val_accuracy: 0.6165
Epoch 19/30
180/180 [==============================] - ETA: 0s - loss: 0.3647 - accuracy: 0.8163
Epoch 19: val_accuracy did not improve from 0.64773
180/180 [==============================] - 9s 49ms/step - loss: 0.3647 - accuracy: 0.8163 - val_loss: 1.3005 - val_accuracy: 0.6193
Epoch 20/30
180/180 [==============================] - ETA: 0s - loss: 0.3488 - accuracy: 0.8293
Epoch 20: val_accuracy did not improve from 0.64773
180/180 [==============================] - 9s 47ms/step - loss: 0.3488 - accuracy: 0.8293 - val_loss: 1.2876 - val_accuracy: 0.6307
Epoch 21/30
179/180 [============================>.] - ETA: 0s - loss: 0.3505 - accuracy: 0.8275
Epoch 21: val_accuracy did not improve from 0.64773
180/180 [==============================] - 9s 48ms/step - loss: 0.3504 - accuracy: 0.8274 - val_loss: 1.4455 - val_accuracy: 0.6222
Epoch 22/30
180/180 [==============================] - ETA: 0s - loss: 0.3343 - accuracy: 0.8271
Epoch 22: val_accuracy did not improve from 0.64773
180/180 [==============================] - 9s 49ms/step - loss: 0.3343 - accuracy: 0.8271 - val_loss: 1.3099 - val_accuracy: 0.6278
Epoch 23/30
179/180 [============================>.] - ETA: 0s - loss: 0.3106 - accuracy: 0.8479
Epoch 23: val_accuracy did not improve from 0.64773
180/180 [==============================] - 9s 48ms/step - loss: 0.3106 - accuracy: 0.8477 - val_loss: 1.3303 - val_accuracy: 0.6108
Epoch 24/30
180/180 [==============================] - ETA: 0s - loss: 0.2927 - accuracy: 0.8502
Epoch 24: val_accuracy did not improve from 0.64773
180/180 [==============================] - 9s 49ms/step - loss: 0.2927 - accuracy: 0.8502 - val_loss: 1.5922 - val_accuracy: 0.6193
Epoch 25/30
180/180 [==============================] - ETA: 0s - loss: 0.2996 - accuracy: 0.8491
Epoch 25: val_accuracy did not improve from 0.64773
180/180 [==============================] - 9s 48ms/step - loss: 0.2996 - accuracy: 0.8491 - val_loss: 1.6287 - val_accuracy: 0.6136
Epoch 26/30
180/180 [==============================] - ETA: 0s - loss: 0.2851 - accuracy: 0.8590
Epoch 26: val_accuracy did not improve from 0.64773
180/180 [==============================] - 9s 48ms/step - loss: 0.2851 - accuracy: 0.8590 - val_loss: 1.6557 - val_accuracy: 0.6165
Epoch 27/30
179/180 [============================>.] - ETA: 0s - loss: 0.2575 - accuracy: 0.8703
Epoch 27: val_accuracy did not improve from 0.64773
180/180 [==============================] - 9s 48ms/step - loss: 0.2578 - accuracy: 0.8696 - val_loss: 1.4593 - val_accuracy: 0.6335
Epoch 28/30
180/180 [==============================] - ETA: 0s - loss: 0.2636 - accuracy: 0.8655
Epoch 28: val_accuracy did not improve from 0.64773
180/180 [==============================] - 9s 48ms/step - loss: 0.2636 - accuracy: 0.8655 - val_loss: 1.5942 - val_accuracy: 0.6250
Epoch 29/30
180/180 [==============================] - ETA: 0s - loss: 0.2580 - accuracy: 0.8661
Epoch 29: val_accuracy did not improve from 0.64773
180/180 [==============================] - 9s 48ms/step - loss: 0.2580 - accuracy: 0.8661 - val_loss: 1.5671 - val_accuracy: 0.6278
Epoch 30/30
180/180 [==============================] - ETA: 0s - loss: 0.2573 - accuracy: 0.8715
Epoch 30: val_accuracy did not improve from 0.64773
Restoring model weights from the end of the best epoch: 15.
180/180 [==============================] - 9s 48ms/step - loss: 0.2573 - accuracy: 0.8715 - val_loss: 1.6954 - val_accuracy: 0.5966
Epoch 30: early stopping


train_acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
train_loss = history.history['loss']
val_loss = history.history['val_loss']

# Create separate plots for accuracy and loss
plt.figure(figsize=(10, 5))

# Accuracy plot
plt.subplot(121)
plt.plot(train_acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.title('Model Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()

# Loss plot
plt.subplot(122)
plt.plot(train_loss, label='Training Loss')
plt.plot(val_loss, label='Validation Loss')
plt.title('Model Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()

plt.tight_layout()
plt.show()


from sklearn.metrics import confusion_matrix, classification_report
from sklearn.metrics import precision_score, recall_score, f1_score



# Evaluate the model_cnn256_3k on the validation dataset
val_loss, val_accuracy = model_cnn256_3k.evaluate(validation_generator, steps=12)
# Predict probabilities for the validation set
val_predictions = model_cnn256_3k.predict(validation_generator, steps=12)
# Convert probabilities to class labels
val_pred_labels = np.argmax(val_predictions, axis=1)
val_true_labels = validation_generator.classes
# Calculate precision, recall, and F1 score manually
precision = precision_score(val_true_labels, val_pred_labels, average='weighted')
recall = recall_score(val_true_labels, val_pred_labels, average='weighted')
f1 = f1_score(val_true_labels, val_pred_labels, average='weighted')


print("Validation Accuracy:", val_accuracy)
print("Precision:", precision)
print("Recall:", recall)
print("F1 Score:", f1)



# Calculate confusion matrix
cm = confusion_matrix(val_true_labels, val_pred_labels)
# Class mapping provided
#class_mapping = val_generator.class_indices
class_mapping = {'Lung Opacity': 0, 'No Lung Opacity / Not Normal': 1, 'Normal': 2}
# Plot confusion matrix with numerical values and categorical labels
plt.figure(figsize=(8, 6))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False,
            xticklabels=class_mapping.keys(), yticklabels=class_mapping.keys())
plt.title('Confusion Matrix')
plt.xlabel('Predicted Labels')
plt.ylabel('True Labels')
plt.show()

12/12 [==============================] - 1s 38ms/step - loss: 1.0112 - accuracy: 0.6417
Validation Accuracy: 0.6416666507720947
Precision: 0.34086204787371327
Recall: 0.3472222222222222
F1 Score: 0.3420621966794381

	patientId	x	y	width	height	Target
0	0004cfab-14fd-4e49-80ba-63a80b6bddd6	NaN	NaN	NaN	NaN	0
1	00313ee0-9eaa-42f4-b0ab-c148ed3241cd	NaN	NaN	NaN	NaN	0
2	00322d4d-1c29-4943-afc9-b6754be640eb	NaN	NaN	NaN	NaN	0
3	003d8fa0-6bf1-40ed-b54c-ac657f8495c5	NaN	NaN	NaN	NaN	0
4	00436515-870c-4b36-a041-de91049b9ab4	264.0	152.0	213.0	379.0	1
...	...	...	...	...	...	...
30222	c1ec14ff-f6d7-4b38-b0cb-fe07041cbdc8	185.0	298.0	228.0	379.0	1
30223	c1edf42b-5958-47ff-a1e7-4f23d99583ba	NaN	NaN	NaN	NaN	0
30224	c1f6b555-2eb1-4231-98f6-50a963976431	NaN	NaN	NaN	NaN	0
30225	c1f7889a-9ea9-4acb-b64c-b737c929599a	570.0	393.0	261.0	345.0	1
30226	c1f7889a-9ea9-4acb-b64c-b737c929599a	233.0	424.0	201.0	356.0	1

	patientId	x	y	width	height	Target	class
0	0004cfab-14fd-4e49-80ba-63a80b6bddd6	NaN	NaN	NaN	NaN	0	No Lung Opacity / Not Normal
1	00313ee0-9eaa-42f4-b0ab-c148ed3241cd	NaN	NaN	NaN	NaN	0	No Lung Opacity / Not Normal
2	00322d4d-1c29-4943-afc9-b6754be640eb	NaN	NaN	NaN	NaN	0	No Lung Opacity / Not Normal
3	003d8fa0-6bf1-40ed-b54c-ac657f8495c5	NaN	NaN	NaN	NaN	0	Normal
4	00436515-870c-4b36-a041-de91049b9ab4	264.0	152.0	213.0	379.0	1	Lung Opacity
...	...	...	...	...	...	...	...
37621	c1ec14ff-f6d7-4b38-b0cb-fe07041cbdc8	185.0	298.0	228.0	379.0	1	Lung Opacity
37623	c1edf42b-5958-47ff-a1e7-4f23d99583ba	NaN	NaN	NaN	NaN	0	Normal
37624	c1f6b555-2eb1-4231-98f6-50a963976431	NaN	NaN	NaN	NaN	0	Normal
37625	c1f7889a-9ea9-4acb-b64c-b737c929599a	570.0	393.0	261.0	345.0	1	Lung Opacity
37627	c1f7889a-9ea9-4acb-b64c-b737c929599a	233.0	424.0	201.0	356.0	1	Lung Opacity

	patientId	x	y	width	height	Target	class
0	0004cfab-14fd-4e49-80ba-63a80b6bddd6	NaN	NaN	NaN	NaN	0	2
1	00313ee0-9eaa-42f4-b0ab-c148ed3241cd	NaN	NaN	NaN	NaN	0	2
2	00322d4d-1c29-4943-afc9-b6754be640eb	NaN	NaN	NaN	NaN	0	2
3	003d8fa0-6bf1-40ed-b54c-ac657f8495c5	NaN	NaN	NaN	NaN	0	0
4	00436515-870c-4b36-a041-de91049b9ab4	264.0	152.0	213.0	379.0	1	1
...	...	...	...	...	...	...	...
37621	c1ec14ff-f6d7-4b38-b0cb-fe07041cbdc8	185.0	298.0	228.0	379.0	1	1
37623	c1edf42b-5958-47ff-a1e7-4f23d99583ba	NaN	NaN	NaN	NaN	0	0
37624	c1f6b555-2eb1-4231-98f6-50a963976431	NaN	NaN	NaN	NaN	0	0
37625	c1f7889a-9ea9-4acb-b64c-b737c929599a	570.0	393.0	261.0	345.0	1	1
37627	c1f7889a-9ea9-4acb-b64c-b737c929599a	233.0	424.0	201.0	356.0	1	1

	patientId	x	y	width	height	Target	class
4	00436515-870c-4b36-a041-de91049b9ab4	264.0	152.0	213.0	379.0	1	1
6	00436515-870c-4b36-a041-de91049b9ab4	562.0	152.0	256.0	453.0	1	1
10	00704310-78a8-4b38-8475-49f4573b2dbb	323.0	577.0	160.0	104.0	1	1
12	00704310-78a8-4b38-8475-49f4573b2dbb	695.0	575.0	162.0	137.0	1	1
18	00aecb01-a116-45a2-956c-08d2fa55433f	288.0	322.0	94.0	135.0	1	1
...	...	...	...	...	...	...	...
37617	c1e73a4e-7afe-4ec5-8af6-ce8315d7a2f2	316.0	504.0	179.0	273.0	1	1
37619	c1ec14ff-f6d7-4b38-b0cb-fe07041cbdc8	609.0	464.0	240.0	284.0	1	1
37621	c1ec14ff-f6d7-4b38-b0cb-fe07041cbdc8	185.0	298.0	228.0	379.0	1	1
37625	c1f7889a-9ea9-4acb-b64c-b737c929599a	570.0	393.0	261.0	345.0	1	1
37627	c1f7889a-9ea9-4acb-b64c-b737c929599a	233.0	424.0	201.0	356.0	1	1

	patientId	x	y	width	height	Target	class	AGE	SEX	ViewPosition	BodyPart	glcm_contrast	glcm_homogeneity	glcm_energy	glcm_correlation
0	0004cfab-14fd-4e49-80ba-63a80b6bddd6	NaN	NaN	NaN	NaN	0	2	51	0	PA	CHEST	240.363107	0.221791	0.041201	0.977566
1	00313ee0-9eaa-42f4-b0ab-c148ed3241cd	NaN	NaN	NaN	NaN	0	2	48	0	PA	CHEST	52.810114	0.333943	0.077472	0.983760
2	00322d4d-1c29-4943-afc9-b6754be640eb	NaN	NaN	NaN	NaN	0	2	19	1	AP	CHEST	220.881035	0.200721	0.025836	0.968193
3	003d8fa0-6bf1-40ed-b54c-ac657f8495c5	NaN	NaN	NaN	NaN	0	0	28	1	PA	CHEST	406.426794	0.165048	0.019650	0.954598
4	00436515-870c-4b36-a041-de91049b9ab4	264.0	152.0	213.0	379.0	1	1	32	0	AP	CHEST	86.499986	0.236534	0.020687	0.986255
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
30222	c1ec14ff-f6d7-4b38-b0cb-fe07041cbdc8	185.0	298.0	228.0	379.0	1	1	54	1	AP	CHEST	251.202459	0.243280	0.057668	0.932018
30223	c1edf42b-5958-47ff-a1e7-4f23d99583ba	NaN	NaN	NaN	NaN	0	0	46	0	PA	CHEST	139.894010	0.238966	0.074233	0.985241
30224	c1f6b555-2eb1-4231-98f6-50a963976431	NaN	NaN	NaN	NaN	0	0	45	1	PA	CHEST	325.506092	0.212639	0.023197	0.924871
30225	c1f7889a-9ea9-4acb-b64c-b737c929599a	570.0	393.0	261.0	345.0	1	1	72	0	AP	CHEST	198.700831	0.227273	0.023604	0.973146
30226	c1f7889a-9ea9-4acb-b64c-b737c929599a	233.0	424.0	201.0	356.0	1	1	72	0	AP	CHEST	198.700831	0.227273	0.023604	0.973146

	patientId	x	y	width	height	Target	class	AGE	SEX	ViewPosition	BodyPart	glcm_contrast	glcm_homogeneity	glcm_energy	glcm_correlation
count	30227	9555.000000	9555.000000	9555.000000	9555.000000	30227.000000	30227.000000	30227.000000	30227.000000	30227	30227	30227.000000	30227.000000	30227.000000	30227.000000
unique	26684	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2	1	NaN	NaN	NaN	NaN
top	3239951b-6211-4290-b237-3d9ad17176db	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	AP	CHEST	NaN	NaN	NaN	NaN
freq	4	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	15297	30227	NaN	NaN	NaN	NaN
mean	NaN	394.047724	366.839560	218.471376	329.269702	0.316108	1.098257	46.797764	0.569557	NaN	NaN	247.026187	0.229017	0.041265	0.962661
std	NaN	204.574172	148.940488	59.289475	157.750755	0.464963	0.821133	16.892940	0.495146	NaN	NaN	136.758147	0.052652	0.044948	0.017100
min	NaN	2.000000	2.000000	40.000000	45.000000	0.000000	0.000000	1.000000	0.000000	NaN	NaN	15.571242	0.091079	0.012571	0.745418
25%	NaN	207.000000	249.000000	177.000000	203.000000	0.000000	0.000000	34.000000	0.000000	NaN	NaN	143.653314	0.193911	0.022976	0.954354
50%	NaN	324.000000	365.000000	217.000000	298.000000	0.000000	1.000000	49.000000	1.000000	NaN	NaN	228.389563	0.222584	0.028748	0.965842
75%	NaN	594.000000	478.500000	259.000000	438.000000	1.000000	2.000000	59.000000	1.000000	NaN	NaN	321.095833	0.255714	0.041918	0.974290
max	NaN	835.000000	881.000000	528.000000	942.000000	1.000000	2.000000	155.000000	1.000000	NaN	NaN	1506.355794	0.733597	0.686796	0.996324

	patientId	x	y	width	height	Target	class	AGE	SEX	ViewPosition	BodyPart	glcm_contrast	glcm_homogeneity	glcm_energy	glcm_correlation	agecat
2774	340e5ce5-5845-4c7d-be4d-a646c7e5c4ed	NaN	NaN	NaN	NaN	0	0	23	1	PA	CHEST	183.368126	0.223974	0.042711	0.981042	Young
7620	5871b176-cee8-4b9b-80e0-a6a4cf36d1b6	NaN	NaN	NaN	NaN	0	0	35	1	PA	CHEST	271.839518	0.188084	0.030167	0.969902	Young
23918	d70cf6a3-da4b-4ecf-a93a-62eaa70312d5	NaN	NaN	NaN	NaN	0	0	46	1	AP	CHEST	156.942711	0.211622	0.028610	0.966852	old
12153	7c897de5-15ff-405c-a77c-c588e7e151c0	NaN	NaN	NaN	NaN	0	0	76	1	PA	CHEST	56.795524	0.289655	0.036799	0.967289	vold
26756	ee4bf94c-e2eb-4ebf-9246-f5f611cd77d8	NaN	NaN	NaN	NaN	0	0	59	1	PA	CHEST	228.136159	0.170099	0.017896	0.968200	old
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
10413	6ef0044f-2fe6-435f-b058-11d2c05c4f81	NaN	NaN	NaN	NaN	0	2	7	0	AP	CHEST	742.363814	0.391316	0.307867	0.932430	child
1535	16d84b55-672c-431b-932e-a2d790ed591a	NaN	NaN	NaN	NaN	0	2	56	1	AP	CHEST	297.546077	0.272186	0.066064	0.959898	old
17536	a75a84bc-8918-4cbd-b8b0-7ccfa8f8e3d2	NaN	NaN	NaN	NaN	0	2	42	1	AP	CHEST	206.591236	0.221989	0.022661	0.960174	Young
22792	cd998117-d583-4316-b9b0-a2a67403ac09	NaN	NaN	NaN	NaN	0	2	38	0	PA	CHEST	372.123037	0.171904	0.021588	0.958739	Young
12405	7ea6a920-a1ad-4cf1-91d5-a6634708b12c	NaN	NaN	NaN	NaN	0	2	69	0	AP	CHEST	129.375030	0.247923	0.044306	0.973732	vold

	patientId	x	y	width	height	Target	class	AGE	SEX	ViewPosition	BodyPart	glcm_contrast	glcm_homogeneity	glcm_energy	glcm_correlation	agecat
1775	18a5c559-5563-47e2-b26a-8b20f7250ab0	NaN	NaN	NaN	NaN	0	0	27	1	PA	CHEST	76.340220	0.276846	0.041549	0.969491	Young
25981	e8214150-a9bb-47f3-99ab-76724ce4af2b	NaN	NaN	NaN	NaN	0	0	58	1	PA	CHEST	227.217347	0.182679	0.037458	0.974232	old
25070	e0dc88d8-9318-4d9c-8c97-e0d6cecd8da9	NaN	NaN	NaN	NaN	0	0	59	1	PA	CHEST	185.424603	0.200336	0.021099	0.966217	old
25404	e3a72ca3-6dfe-4cfa-98a7-7eb566ff13ed	NaN	NaN	NaN	NaN	0	0	41	1	PA	CHEST	247.851294	0.204559	0.020587	0.966127	Young
26640	ed369fe4-b5e2-4ac4-8bef-ac7f5e18042b	NaN	NaN	NaN	NaN	0	0	12	1	AP	CHEST	264.433074	0.272203	0.092705	0.970542	child
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
763	0a23debc-fa30-486f-9555-750be017ae75	NaN	NaN	NaN	NaN	0	2	47	1	PA	CHEST	363.677512	0.160608	0.016582	0.954540	old
26281	eaaee470-cf44-4d79-a5f5-4e8b642fc999	NaN	NaN	NaN	NaN	0	2	24	1	AP	CHEST	70.985622	0.279741	0.043673	0.982625	Young
5346	462a48b2-bcf4-486b-b082-75c91b9f1fb0	NaN	NaN	NaN	NaN	0	2	19	1	AP	CHEST	209.637471	0.240531	0.064383	0.968633	teenage
4249	3d9fe022-f01e-4685-893c-6f6f6eac2fa1	NaN	NaN	NaN	NaN	0	2	20	0	PA	CHEST	164.854973	0.259244	0.045919	0.957367	Young
10241	6da20c13-b4ce-4009-af02-191262a92791	NaN	NaN	NaN	NaN	0	2	42	0	PA	CHEST	221.568034	0.205700	0.027079	0.954955	Young

	patientId	x	y	width	height	Target	class	AGE	SEX	ViewPosition	BodyPart	glcm_contrast	glcm_homogeneity	glcm_energy	glcm_correlation	agecat
16289	9de81172-a002-46fc-b17c-8701f04c2647	NaN	NaN	NaN	NaN	0	0	47	0	PA	CHEST	605.074205	0.320357	0.137987	0.946489	old
15623	987237ff-671d-4142-93de-64036ccc6d8d	NaN	NaN	NaN	NaN	0	0	46	1	AP	CHEST	159.388366	0.214131	0.027863	0.964438	old
10550	700dda66-d2a5-4898-8f6e-78ef63cbd2a3	NaN	NaN	NaN	NaN	0	0	51	0	PA	CHEST	484.081672	0.178203	0.019940	0.911100	old
25973	e811705a-0486-406a-aa0b-3c5ea99e2b1b	NaN	NaN	NaN	NaN	0	0	58	0	PA	CHEST	98.155896	0.253060	0.029793	0.966974	old
15155	947e8c14-5f0c-4ac6-b6e8-cd1d06bfe1eb	NaN	NaN	NaN	NaN	0	0	43	1	PA	CHEST	134.330894	0.218163	0.025026	0.960076	Young
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
12777	81a02f7b-228e-4b15-a0ba-21df6ae131ef	NaN	NaN	NaN	NaN	0	2	64	0	PA	CHEST	192.862565	0.202584	0.021155	0.969673	vold
20744	bd04fa63-5ecd-42da-bf2f-6214896bfc3e	NaN	NaN	NaN	NaN	0	2	65	1	PA	CHEST	425.011206	0.234750	0.030998	0.951107	vold
29349	12b8f793-1aad-426a-b116-5508be9b41a3	NaN	NaN	NaN	NaN	0	2	30	1	AP	CHEST	102.990498	0.239806	0.022546	0.980153	Young
2259	2e776901-d47c-4d8e-9b8c-04587230ef92	NaN	NaN	NaN	NaN	0	2	59	0	AP	CHEST	133.688903	0.255747	0.027001	0.979462	old
14220	8d0c5b73-145f-47f1-8e50-563b44e4cf7b	NaN	NaN	NaN	NaN	0	2	48	1	PA	CHEST	211.343779	0.180150	0.020221	0.974004	old